Predictive Aircraft Load Alleviation for Gust and Maneuver Response
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Solution Overview
Problem
Existing aerospace vehicle load alleviation systems rely on feedback control, which can lead to instability and delays in responding to wind gusts and maneuvers, resulting in increased fuel consumption, structural stress, and reduced operational efficiency due to the need for heavy filtering and additional equipment.
Innovation Solution
A predictive system using a sensor and a predictor with an algorithm that estimates future loads on the vehicle, allowing for preemptive control actions through a control element like spoilers or elevators, reducing the reliance on notch and non-linear filters to minimize delays and instability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If feedback control systems are used for load alleviation, then the system can respond to wind gusts and maneuvers, but the response time is delayed and instability occurs due to the need for heavy filtering
Solution Approach 1:
The system performs preliminary action by predicting future loads before they occur. The predictor estimates upcoming load conditions based on current state measurements, allowing the control system to prepare and respond in advance rather than reacting after the load has already impacted the structure. This eliminates the need for delayed feedback filtering while maintaining stability.
Solution Approach 2:
The system uses state feedback from sensors to continuously monitor the current condition of the aerospace vehicle. This feedback is fed into the predictor which uses it to forecast future loads. The combination of real-time feedback and predictive modeling enables the system to maintain stability while responding faster than traditional feedback-only systems.
2Measurement precision
If notch and non-linear filters are used in load alleviation systems, then filtering is improved, but the system complexity and equipment requirements increase
Solution Approach 1:
The system replaces complex mechanical filtering hardware (notch filters and non-linear filters) with a computational predictor based on mathematical models and algorithms. The predictor uses state-space representations and algebraic equations to achieve filtering precision through software computation rather than physical filter components, significantly reducing system complexity and equipment requirements.
3Productivity
If predictive algorithms are used to estimate future loads, then response time is reduced and stability is improved, but computational requirements increase
Solution Approach 1:
The system changes parameters by using simplified state-space models and algebraic equations that require minimal computational resources. The predictor focuses on estimating key parameters (future loads) using linear relationships and pre-computed system matrices, avoiding complex iterative algorithms. This enables real-time prediction with modest computational power while maintaining high operational efficiency.
Data Source
AI summary
A process and machine configured to predict and preempt an undesired load and/or bending moment on a part of a vehicle resulting from an exogenous or a control input. The machine may include a predictor with an algorithm for converting parameters from a state sensed upwind from the part into an estimated normal load on the part and a prediction, for a future time, of a normal load scaled for a weight of the aerospace vehicle. The machine may: produce, using a state upwind from the part on the aerospace vehicle and/or a maneuver input, a predicted state, load and bending moment on the part at a time in the future; derive a command preempting the part from experiencing the predicted load and bending moment; and actuate the command just prior to the part experiencing the predicted state, thereby alleviating the part from experiencing the predicted load and bending moment.